Sir, they have blocked access to the very AI models you said we should share instead of build

Invincible has a much better season ending than The Boys.😒

McKinsey, BCG, Accenture, Deloitte, and a few more, signed up to use the software, and also agreed to embed OpenAI's FDEs directly into their client teams. On the surface, it's the ultimate win-win... but deep down it looks like a Trojan Horse. The issue of the original Trojan Horse was two-fold: the Greeks were inside, and the Trojans had nowhere left to go. The risk in the enterprise is that by the time the consultants realize they have automated 80% of their own intellectual property, it will be too late: they will have already trained the models that replace them. They will have given OpenAI and Anthropic the direct relationships with the C-suites. I sometimes think Sam and Dario are not your friends, they did not come to help you build a better consulting firm. They came to build a world where the "consultant" is just a legacy function - a ghost in the machine that was once a person. More on my recent article on Consulting Intel:

Locked in for my next project!

We no longer have skill issues, we have skill-md issues.

stay delusional, your win is coming

In 2003, after a thorough study of the Mahabharata, Giampaolo Thomasetti began work on a large-scale project dedicated to it. After 12 years, he completed his collection of over 20 majestic paintings depicting the main moments of this great spiritual epic. 👇

Software development is a commodity now.

We are building the core team at Vanar Robots. Looking for: • Electronics (end-to-end) • Software & AI (VLA, autonomy, sim2real) For people who build even when things seem impossible. DM me or write to us at hello@vanarrobots.com

I think we need to start building documentation not for humans first, but for AI.

learning AI skills so that i can stay ahead in a changing world

Abhi gore aise dominate kar rahe hote toh Indian brown sepoys would have been like, 'Indians can only dream of playing this brand of cricket.'

So who discovered the Fibonacci sequence? Fibonacci? No, afraid not (no disrespect Leonardo Bonacci, aka Fibonacci). Another scientist? Nope. Who then? Here's @MarcusduSautoy (it wasn't him either in case you're wondering).

We’re spending $200B a year on data centers to power AI. One company raised $11M, grew human brain cells on a chip, and the cells taught themselves to play a 3D shooter in a week. Cortical Labs grew 200,000 human neurons on a silicon chip and taught them to play Doom. The cells navigate, target enemies, and fire weapons in real time. Their previous game, Pong, took 18 months on older hardware. Doom took a week. An independent developer with zero biotech experience built the integration using a Python API. The neurons did the rest. That compression from 18 months to one week tells you everything about where this is going. Here’s what the “can it run Doom” crowd is missing: each CL1 unit costs $35,000. A full 30-unit server rack draws 850 to 1,000 watts total. Your brain runs on 20 watts. A single GPU cluster training an LLM can draw megawatts. The energy economics of biological compute are orders of magnitude better than silicon, and that gap scales. The investor list tells you who’s paying attention. Horizons Ventures, Blackbird, and In-Q-Tel, the CIA’s venture arm. In-Q-Tel doesn’t fund science projects. They fund intelligence infrastructure. 115 units started shipping in 2025. Cortical Labs is now selling “Wetware-as-a-Service” through the Cortical Cloud. Developers can deploy code to living neurons remotely without touching a lab. They’re pricing access at the level of a software subscription while the hardware runs on real human brain cells derived from adult skin and blood samples. The Doom demo is marketing. The platform play is a bet that biological neurons will eventually outperform silicon at exactly the tasks AI struggles with most: real-time adaptation under uncertainty, learning from minimal data, and processing ambiguity without brute-force compute. The question was never “can it run Doom.” The question is what happens when it can run everything else.

Indian model Sarvam-105b is really really good Sarvam AI has open-sourced two India-built reasoning models, Sarvam 30B and 105B, positioning them as globally competitive open models. The big unlock is not just benchmark scores like 98.6 on Math500 for 105B or strong local deployment efficiency for 30B, but the full-stack story: in-house data, training, RL, tokenizer design, and inference optimization built for both frontier GPUs and consumer devices.

A universal truth: most radar charts should just be bar charts. Love your stuff, Anthropic!